# Deterministic tests. No burned credits, no side effects.

The sandbox proxy records your agent's real tool and API traffic as fixtures inside the trace. Replay them and the same input exercises the same path: flaky APIs frozen, paid endpoints free, refunds not actually issued.

## The problem: You can't unit-test against the live internet

Real agent tests hit real APIs: flaky search endpoints, metered LLM calls, tools with side effects. So test suites get skipped, and the first real test of a prompt change is production.

## How RunAgain solves it

1. **Fixtures recorded from reality.** Drop the transparent proxy into the agent's sandbox with one command, agent unchanged, and every outbound HTTP call is captured as a span and stored as a fixture, matched to its trace by trace-context propagation.
2. **Replay instead of re-call.** Serve the stored responses back and a run becomes deterministic: same tools, same results, every time. Iterate on prompts and models without spending a cent on the tool side.
3. **Regression suites from production.** Capture real traces, including the ones that failed, into versioned datasets. Your test cases are things that actually happened, not things you imagined might.
4. **CI gating on scores.** The runagain-eval CLI runs an experiment in CI and fails the build when a scorer drops below threshold or regresses significantly against a baseline. Statistically, not vibes: McNemar-tested, so noise doesn't block merges.

## Related solutions

- [Simulation & evals](https://runagain.ai/solutions/agent-simulation-evals.md)
- [Experimentation](https://runagain.ai/solutions/agent-experimentation.md)
- [Debug AI agents](https://runagain.ai/solutions/debug-ai-agents.md)

Book a demo: https://cal.com/tamas-szuromi/30min · Get in touch: tamas@runagain.ai

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Markdown mirror of https://runagain.ai/solutions/mocked-environments for agents and LLMs. Append .md to any runagain.ai page URL for its markdown twin. Overview: https://runagain.ai/llms.txt · For agents: https://runagain.ai/for-agents.md